2022 Reading List

A much shorter list of books this year, as I’m reading more academic papers instead

Books

• Limbo (Lubrano, 2003)
• The Ethical Algorithm (Kearns & Roth, 2019)

Audiobooks

• Virtual Society (Narula, 2022)
• Black Spartacus (Hazareesingh, 2020)
• Solve For Happy (Gawdat, 2017)
• When Prophecy Fails (Festinger et al, 1956)
• Indistractable (Eyal, 2019)
• Nice Girls Don’t Get the Corner Office (Frankel, 2010)

Papers

• Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review, 1(1).
• Narayanan, A. & Shmatikov, V. (2008). Robust De-anonymization of Large Sparse Datasets. 2008 IEEE Symposium on Security and Privacy (sp 2008). 111–125.
• Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. PMLR Conference on fairness, accountability and transparency (pp. 77-91).
• Awad, E., Dsouza, S., Kim, R. et al. The Moral Machine experiment. Nature 563, 59–64 (2018).
• Ribeiro, M. T., Singh, S. & Guestrin, C. (2016). “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1135-1144.
• Ameisen, E. (2020). (Chapter 11). In Building machine learning powered applications: going from idea to product. First edition. O’Reilly Media, Inc.